homerquan/DrugClip
https://huggingface.co/homerquan/DrugClipDrugCLIP is a dual-encoder multimodal model (SchNet 3D Graph Neural Network + DistilBERT Text Encoder) mapped to a shared 128-dimensional latent space. It is designed to evaluate and retrieve novel 3D molecular structures by aligning them with natural language therapeutic intents and clinical…
Sourced from
- HuggingFace — homerquan/DrugClip
Related resources
FremyCompany/BioLORD-2023-M
by FremyCompany# FremyCompany/BioLORD-2023-M This model was trained using BioLORD, a new pre-training strategy for producing meaningful representations for clinical sentences and biomedical concepts.
valencelabs/mars-fm
by valencelabsThis repository contains the PyTorch model weights for MarS-FM (Markov Space Flow Matching) trained on the MD-CATH dataset. This model was introduced in the ICLR 2026 paper: MarS-FM: Generative Modeling of Molecular Dynamics via Markov State Models.
zhihan1996/DNABERT-S
by zhihan1996tahoebio/Tahoe-x1
by tahoebioTahoe-x1 is a family of perturbation-trained single-cell foundation models with up to 3 billion parameters, developed by Tahoe Therapeutics. Pretrained on 266 million single-cell transcriptomic profiles including the Tahoe-100M perturbation compendium, Tahoe-x1 achieves state-of-the-art performance…